Multi-component peptide hydrogels – a systematic study incorporating biomolecules for the exploration of diverse, tuneable biomaterials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peptide-based supramolecular gels can be designed to be functional "smart" materials that have applications in drug delivery, tissue engineering, and supramolecular chemistry. Although many multi-component gel systems have been designed and reported, many of these applications still rely solely on single-component gel systems which limits the functionalities of the materials. Multi-component self-assembly leads to the formation of highly ordered and complex architectures while offering the possibility to generate hydrogels with interesting properties including functional complexity and diverse morphologies. Being able to incorporate various classes of biomolecules can allow for tailoring the materials' functionalities to specific application needs. Here, a novel peptide amphiphile, myristyl-Phe-Phe (C14-FF), was synthesized and explored for hydrogel formation. The hydrogel possesses a nanofiber matrix morphology, composed of β-sheet aggregates, a record-low gelation concentration for this class of compounds, and a unique solvent-dependent helical switch. The C14-FF hydrogel was then explored with various classes of biomolecules (carbohydrates, vitamins, proteins, building blocks of HA) to generate a multi-component library of gels that have potential to represent the complex natural extracellular matrix. Selected multi-component gels exhibit an excellent compatibility with mesenchymal stem cells showing high cell viability percentages, which holds great promise for applications in regenerative therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it